Methods and apparatus for semiconductor testing

ABSTRACT

A method and apparatus for testing semiconductors according to various aspects of the present invention comprises a test system comprising an outlier identification element configured to identify significant data in a set of test results. The test system may be configured to provide the data in an output report. The outlier identification element suitably performs the analysis at run time. The outlier identification element may also operate in conjunction with a smoothing system to smooth the data and identify trends and departures from test result norms.

CROSS-REFERENCES TO RELATED APPLICATIONS

[0001] This application is a continuation-in-part of U.S. patentapplication Ser. No. 09/872,195, filed on May 31, 2001, entitled METHODSAND APPARATUS FOR DATA SMOOTHING, and claims the benefit of U.S.Provisional Patent Application No. 60/293,577, filed May 24, 2001,entitled METHODS AND APPARATUS FOR DATA SMOOTHING; U.S. ProvisionalPatent Application No. 60/295,188, filed May 31, 2001, entitled METHODSAND APPARATUS FOR TEST DATA CONTROL AND ANALYSIS; and U.S. ProvisionalPatent Application No. 60/374,328, filed Apr. 21, 2002, entitled METHODSAND APPARATUS FOR TEST PROGRAM ANALYSIS AND ENHANCEMENT; and

[0002] incorporates the disclosure of each application by reference. Tothe extent that the present disclosure conflicts with any referencedapplication, however, the present disclosure is to be given priority.

FIELD OF THE INVENTION

[0003] The invention relates to semiconductor testing.

BACKGROUND OF THE INVENTION

[0004] Semiconductor companies test components to ensure that thecomponents operate properly. The test data not only determine whetherthe components function properly, but also may indicate deficiencies inthe manufacturing process. Accordingly, many semiconductor companies mayanalyze the collected data from several different components to identifyproblems and correct them. For example, the company may gather test datafor multiple chips on each wafer among several different lots. This datamay be analyzed to identify common deficiencies or patterns of defectsor identify parts that may exhibit quality and performance issues and toidentify or classify user-defined “good parts”. Steps may then be takento correct the problems. Testing is typically performed before devicepackaging (at wafer level) as well as upon completion of assembly (finaltest).

[0005] Gathering and analyzing test data is expensive and timeconsuming. Automatic testers apply signals to the components and readthe corresponding output signals. The output signals may be analyzed todetermine whether the component is operating properly. Each testergenerates a large volume of data. For example, each tester may perform200 tests on a single component, and each of those tests may be repeated10 times. Consequently, a test of a single component may yield 2000results. Because each tester is testing 100 or more components an hourand several testers may be connected to the same server, an enormousamount of data must be stored. Further, to process the data, the servertypically stores the test data in a database to facilitate themanipulation and analysis of the data. Storage in a conventionaldatabase, however, requires further storage capacity as well as time toorganize and store the data.

[0006] The analysis of the gathered data is also difficult. The volumeof the data may demand significant processing power and time. As aresult, the data is not usually analyzed at product run time, but isinstead typically analyzed between test runs or in other batches.

[0007] To alleviate some of these burdens, some companies only samplethe data from the testers and discard the rest. Analyzing less than allof the data, however, ensures that the resulting analysis cannot befully complete and accurate. As a result, sampling degrades the completeunderstanding of the test results.

[0008] In addition, acquiring the test data presents a complex andpainstaking process. A test engineer prepares a test program to instructthe tester to generate the input signals to the component and receivethe output signals. The program tends to be very complex to ensure fulland proper operation of the component. Consequently, the test programfor a moderately complex integrated circuit involves a large number oftests and results. Preparing the program demands extensive design andmodification to arrive at a satisfactory solution, and optimization ofthe program, for example to remove redundant tests or otherwise minimizetest time, requires additional exertion.

SUMMARY OF THE INVENTION

[0009] A method and apparatus for testing semiconductors according tovarious aspects of the present invention comprises a test systemcomprising an outlier identification element configured to identifysignificant data in a set of test results. The test system may beconfigured to provide the data in an output report. The outlieridentification element suitably performs the analysis at run time. Theoutlier identification element may also operate in conjunction with asmoothing system to smooth the data and identify trends and departuresfrom test result norms.

BRIEF DESCRIPTION OF THE DRAWING

[0010] A more complete understanding of the present invention may bederived by referring to the detailed description and the claims whenconsidered in connection with the following illustrative figures, whichmay not be to scale. Like reference numbers refer to similar elementsthroughout the figures.

[0011]FIG. 1 is a block diagram of a test system according to variousaspects of the present invention and associated functional components;

[0012]FIG. 2 is a block diagram of elements for operating the testsystem;

[0013]FIG. 3 illustrates a flow chart for a configuration element;

[0014] FIGS. 4A-C illustrate a flow chart for a supplemental dataanalysis element;

[0015]FIG. 5 is a diagram of various sections of a wafer;

[0016] FIGS. 6A-B further illustrate a flow chart for a supplementaldata analysis element;

[0017]FIG. 7 illustrates a flow chart for an output element;

[0018]FIG. 8 is a flow chart for operation of an exemplary datasmoothing system according to various aspects of the present invention;

[0019]FIG. 9 is a plot of test data for a test of multiple components;

[0020]FIG. 10 is a representation of a wafer having multiple devices anda resistivity profile for the wafer;

[0021]FIG. 11 is a graph of resistance values for a population ofresistors in the various devices of the wafer of FIG. 10; and

[0022] FIGS. 12A-B are general and detailed plots, respectively, of rawtest data and smoothed test data for the various devices of FIG. 10.

[0023] Elements in the figures are illustrated for simplicity andclarity and have not necessarily been drawn to scale. For example, theconnections and steps performed by some of the elements in the figuresmay be exaggerated or omitted relative to other elements to help toimprove understanding of embodiments of the present invention.

DETAILED DESCRIPTION OF AN EXEMPLARY EMBODIMENT

[0024] The present invention may be described in terms of functionalblock components and various process steps. Such functional blocks andsteps may be realized by any number of hardware or software componentsconfigured to perform the specified functions. For example, the presentinvention may employ various testers, processors, storage systems,processes, and integrated circuit components, e.g., statistical engines,memory elements, signal processing elements, logic elements, programs,and the like, which may carry out a variety of functions under thecontrol of one or more testers, microprocessors, or other controldevices. In addition, the present invention may be practiced inconjunction with any number of test environments, and each systemdescribed is merely one exemplary application for the invention.Further, the present invention may employ any number of conventionaltechniques for data analysis, component interfacing, data processing,component handling, and the like.

[0025] Referring to FIG. 1, a method and apparatus according to variousaspects of the present invention operates in conjunction with a testsystem 100 having a tester 102, such as automatic test equipment (ATE)for testing semiconductors. In the present embodiment, the test system100 comprises a tester 102 and a computer system 108. The test system100 may be configured for testing any components 106, such assemiconductor devices on a wafer, circuit boards, packaged devices, orother electrical or optical systems. In the present embodiment, thecomponents 106 comprise multiple integrated circuit dies formed on awafer or packaged integrated circuits or devices.

[0026] The tester 102 suitably comprises any test equipment that testscomponents 106 and generates output data relating to the testing. Thetester 102 may comprise a conventional automatic tester, such as aTeradyne tester, and suitably operates in conjunction with otherequipment for facilitating the testing. The tester 102 may be selectedand configured according to the particular components 106 to be testedand/or any other appropriate criteria.

[0027] The tester 102 may operate in conjunction with the computersystem 108 to, for example, program the tester 102, load and/or executethe test program, collect data, provide instructions to the tester 102,implement a statistical engine, control tester parameters, and the like.In the present embodiment, the computer system 108 receives tester datafrom the tester 102 and performs various data analysis functionsindependently of the tester 102. The computer system 108 also implementsa statistical engine to analyze data from the tester 102. The computersystem 108 may comprise a separate computer, such as a personal computeror workstation, connected to or networked with the tester 102 toexchange signals with the tester 102. In an alternative embodiment, thecomputer system 108 may be omitted from or integrated into othercomponents of the test system 100 and various functions may be performedby other components, such as the tester 102.

[0028] The computer system 108 includes a processor 110 and a memory112. The processor 110 comprises any suitable processor, such as aconventional Intel, Motorola, or Advanced Micro Devices processor,operating in conjunction with any suitable operating system, such asWindows 98, Windows NT, Unix, or Linux. Similarly, the memory 112 maycomprise any appropriate memory accessible to the processor 110, such asa random access memory (RAM) or other suitable storage system, forstoring data. In particular, the memory 112 of the present systemincludes a fast access memory for storing and receiving information andis suitably configured with sufficient capacity to facilitate theoperation of the computer 108.

[0029] In the present embodiment, the memory 112 includes capacity forstoring output results received from the tester 102 and facilitatinganalysis of the output test data. The memory 112 is configured for faststorage and retrieval of test data for analysis. The memory 112 issuitably configured to store the elements of a dynamic datalog, suitablycomprising a set of information selected by the test system 100 and/orthe operator according to selected criteria and analysis based on thetest results.

[0030] For example, the memory 112 suitably stores a componentidentifier for each component 106, such as x-y coordinates correspondingto a position of the component 106 on a wafer map for the tested wafer.Each x-y coordinate in the memory 112 may be associated with aparticular component 106 at the corresponding x-y coordinate on thewafer map. Each component identifier has one or more fields, and eachfield corresponds, for example, to a particular test performed on thecomponent 106 at the corresponding x-y position on the wafer, astatistic related to the corresponding component 106, or other relevantdata. The memory 112 may be configured to include any data identified bythe user as desired according to any criteria or rules.

[0031] The computer 108 of the present embodiment also suitably hasaccess to a storage system, such as another memory (or a portion of thememory 112), a hard drive array, an optical storage system, or othersuitable storage system. The storage system may be local, like a harddrive dedicated to the computer 108 or the tester 102, or may be remote,such as a hard drive array associated with a server to which the testsystem 100 is connected. The storage system may store programs and/ordata used by the computer 108 or other components of the test system100. In the present embodiment, the storage system comprises a database114 available via a remote server 116 comprising, for example, a mainproduction server for a manufacturing facility. The database 114 storestester information, such as tester data files, master data files foroperating the test system 100 and its components, test programs,downloadable instructions for the test system 100, and the like.

[0032] The test system 100 may include additional equipment tofacilitate testing of the components 106. For example, the present testsystem 100 includes a device interface 104, like a conventional deviceinterface board and/or a device handler or prober, to handle thecomponents 106 and provide an interface between the components 106 andthe tester 102. The test system 100 may include or be connected to othercomponents, equipment, software, and the like to facilitate testing ofthe components 106 according to the particular configuration,application, environment of the test system 100, or other relevantfactors. For example, in the present embodiment, the test system 100 isconnected to an appropriate communication medium, such as a local areanetwork, intranet, or global network like the internet, to transmitinformation to other systems, such as the remote server 116.

[0033] The test system 100 may include one or more testers 102 and oneor more computers 108. For example, one computer 108 may be connected toan appropriate number of, such as up to twenty or more, testers 102according to various factors, such as the system's throughput and theconfiguration of the computer 108. Further, the computer 108 may beseparate from the tester 102, or may be integrated into the tester 102,for example utilizing one or more processors, memories, clock circuits,and the like of the tester 102 itself. In addition, various functionsmay be performed by different computers. For example, a first computermay perform various pre-analysis tasks, several computers may thenreceive the data and perform data analysis, and another set of computersmay prepare the dynamic datalogs and/or other output reports.

[0034] A test system 100 according to various aspects of the presentinvention tests the components 106 and provides enhanced analysis andtest results. For example, the supplemental analysis may identifyincorrect, questionable, or unusual results, repetitive tests, and/ortests with a relatively high probability of failure. The operator, suchas the product engineer, test engineer, manufacturing engineer, deviceengineer, or other personnel using the test data, may then use theresults to verify and/or improve the test system 100 and classifycomponents 106.

[0035] The test system 100 according to various aspects of the presentinvention executes an enhanced test process for testing the components106 and collecting and analyzing test data. The test system 100 suitablyoperates in conjunction with a software application executed by thecomputer 108. Referring to FIG. 2, the software application of thepresent embodiment includes multiple elements for implementing theenhanced test process, including a configuration element 202, asupplementary data analysis element 206, and an output element 208. Eachelement 202, 206, 208 suitably comprises a software module operating onthe computer 108 to perform various tasks. Generally, the configurationelement 202 prepares test system 100 for testing and analysis. In thesupplementary data analysis element 206, output test data from thetester 102 is analyzed to generate supplementary test data, suitably atrun time and automatically. The supplementary test data is thentransmitted to the operator or another system by the output element 208.

[0036] The configuration element 202 configures the test system 100 fortesting the components 106 and analyzing the test data. The test system100 suitably uses a predetermined set of initial parameters and, ifdesired, information from the operator to configure the test system 100.The test system 100 is suitably initially configured with predeterminedor default parameters to minimize operator attendance to the test system100. Adjustments may be made to the configuration by the operator, ifdesired, for example via the computer 108.

[0037] Referring to FIG. 3, an exemplary configuration process 300performed by the configuration element 202 begins with an initializationprocedure (step 302) to set the computer 108 in an initial state. Theconfiguration element 202 then obtains application configurationinformation (step 304), for example from the database 114, for thecomputer 108 and the tester 102. For example, the configuration element202 may access a master configuration file for the enhanced test processand/or a tool configuration file relating to the tester 102. The masterconfiguration file may contain data relating to the proper configurationfor the computer 108 and other components of the test system 100 toexecute the enhanced test process. Similarly, the tool configurationfile suitably includes data relating to the tester 102 configuration,such as connection, directory, IP address, tester node identification,manufacturer, flags, prober identification, or any other pertinentinformation for the tester 102.

[0038] The configuration element 202 may then configure the test system100 according to the data contained in the master configuration fileand/or the tool configuration file (step 306). In addition, theconfiguration element 202 may use the configuration data to retrievefurther relevant information from the database 114, such as the tester's102 identifier (step 308) for associating data like logistics instancesfor tester data with the tester 102. The test system 100 informationalso suitably includes one or more default parameters that may beaccepted, declined, or adjusted by the operator. For example, the testsystem 100 information may include global statistical process control(SPC) rules and goals that are submitted to the operator uponinstallation, configuration, power-up, or other appropriate time forapproval and/or modification. The test system 100 information may alsoinclude default wafer maps or other files that are suitably configuredfor each product, wafer, component 106, or other item that may affect orbe affected by the test system 100. The configuration algorithms,parameters, and any other criteria may be stored in a recipe file foreasy access, correlation to specific products and/or tests, and fortraceability.

[0039] When the initial configuration process is complete, the testsystem 100 commences a test run, for example in conjunction with aconventional series of tests, in accordance with a test program. Thetester 102 suitably executes the test program to apply signals toconnections on the components 106 and read output test data from thecomponents 106. The tester 102 may perform multiple tests on eachcomponent 106 on a wafer, and each test may be repeated several times onthe same component 106. Test data from the tester 102 is stored forquick access and supplemental analysis as the test data is acquired. Thedata may also be stored in a long-term memory for subsequent analysisand use.

[0040] Each test generates at least one result for at least one of thecomponents. Referring to FIG. 9, an exemplary set of test results for asingle test of multiple components comprises a first set of test resultshaving statistically similar values and a second set of test resultscharacterized by values that stray from the first set. Each test resultmay be compared to an upper test limit and a lower test limit. If aparticular result for a component exceeds either limit, the componentmay be classified as a “bad part”.

[0041] Some of the test results in the second set that stray from thefirst set may exceed the control limits, while others do not. For thepresent purposes, those test results that stray from the first set butdo not exceed the control limits or otherwise fail to be detected arereferred to as “outliers”. The outliers in the test results may beidentified and analyzed for any appropriate purpose, such as to identifypotentially unreliable components. The outliers may also be used toidentify a various potential problems and/or improvements in the testand manufacturing processes.

[0042] As the tester 102 generates the test results, the output testdata for each component, test, and repetition is stored by the tester102 in a tester data file. The output test data received from eachcomponent 106 is analyzed by the tester 102 to classify the performanceof the component 106, for example by comparison to the upper and lowertest limits, and the results of the classification are also stored inthe tester data file. The tester data file may include additionalinformation as well, such as logistics data and test programidentification data. The tester data file is then provided to thecomputer 108 in an output file, such as a standard tester data format(STDF) file, and stored in memory.

[0043] When the computer 108 receives the tester data file, thesupplementary data analysis element 206 analyzes the data to provideenhanced output results. The supplementary data analysis element 206 mayprovide any appropriate analysis of the tester data to achieve anysuitable objective. For example, the supplementary data analysis element206 may implement a statistical engine for analyzing the output testdata at run time and identifying data and characteristics of the data ofinterest to the operator. The data and characteristics identified may bestored, while data that is not identified may be otherwise disposed of,such as discarded.

[0044] The supplementary data analysis element 206 may, for example,calculate statistical figures according to the data and a set ofstatistical configuration data. The statistical configuration data maycall for any suitable type of analysis according to the needs of thetest system 100 and/or the operator, such as statistical processcontrol, outlier identification and classification, signature analyses,and data correlation. Further, the supplementary data analysis element206 suitably performs the analysis at run time, i.e., within a matter ofseconds or minutes following generation of the test data. Thesupplementary data analysis element 206 may also perform the analysisautomatically with minimal intervention from the operator and/or testengineer.

[0045] In the present test system 100, after the computer 108 receivesand stores the tester data file, the supplementary data analysis element206 performs various preliminary tasks to prepare the computer 108 foranalysis of the output test data and facilitate generation ofsupplementary data and preparation of an output report. Referring now toFIGS. 4A-C, in the present embodiment, the supplementary data analysiselement 206 initially copies the tester data file to a tool inputdirectory corresponding to the relevant tester 102 (step 402). Thesupplementary data analysis element 206 also retrieves configurationdata to prepare the computer 108 for supplementary analysis of theoutput test data.

[0046] The configuration data suitably includes a set of logistics datathat may be retrieved from the tester data file (step 404). Thesupplementary data analysis element 206 also creates a logisticsreference (step 406). The logistics reference may include tester 102information, such as the tester 102 information derived from the toolconfiguration file. In addition, the logistics reference is assigned anidentification.

[0047] The configuration data may also include an identifier for thetest program that generated the output test data. The test program maybe identified in any suitable manner, such as looking it up in thedatabase 114 (step 408), by association with the tester 102identification, or reading it from the master configuration file. If notest program identification can be established (step 410), a testprogram identification may be created and associated with the testeridentification (step 412).

[0048] The configuration data further identifies the wafers in the testrun to be processed by the supplementary data analysis element 206, iffewer than all of the wafers. In the present embodiment, thesupplementary data analysis element 206 accesses a file indicating whichwafers are to be analyzed (step 414). If no indication is provided, thecomputer 108 suitably defaults to analyzing all of the wafers in thetest run.

[0049] If the wafer for the current test data file is to be analyzed(step 416), the supplementary data analysis element 206 proceeds withperforming the supplementary data analysis on the test data file for thewafer. Otherwise, the supplementary data analysis element 206 waits foror accesses the next test data file (step 418).

[0050] The supplementary data analysis element 206 may establish one ormore section groups to be analyzed for the various wafers to be tested(step 420). To identify the appropriate section group to apply to theoutput test data, the supplementary data analysis element 206 suitablyidentifies an appropriate section group definition, for exampleaccording to the test program and/or the tester identification. Eachsection group includes one or more section arrays, and each sectionarray includes one or more sections of the same section types.

[0051] Section types comprise various sorts of component 106 groupspositioned in predetermined areas of the wafer. For example, referringto FIG. 5, a section type may include a row 502, a column 504, a stepperfield 506, a circular band 508, a radial zone 510, a quadrant 512, orany other desired grouping of components. Different section types may beused according to the configuration of the components, such as order ofcomponents processed, sections of a tube, or the like. Such groups ofcomponents 106 are analyzed together to identify, for example, commondefects or characteristics that may be associated with the group. Forexample, if a particular portion of the wafer does not conduct heat likeother portions of the wafer, the test data for a particular group ofcomponents 106 may reflect common characteristics or defects associatedwith the uneven heating of the wafer.

[0052] Upon identifying the section group for the current tester datafile, the supplemental data analysis element 206 retrieves any furtherrelevant configuration data, such as control limits and enable flags forthe test program and/or tester 102 (step 422). In particular, thesupplemental data analysis element 206 suitably retrieves a set ofdesired statistics or calculations associated with each section array inthe section group (step 423). Desired statistics and calculations may bedesignated in any manner, such as by the operator or retrieved from afile. Further, the supplemental data analysis element 206 may alsoidentify one or more signature analysis algorithms (step 424) for eachrelevant section type or other appropriate variation relating to thewafer and retrieve the signature algorithms from the database 114 aswell.

[0053] All of the configuration data may be provided by default orautomatically accessed by the configuration element 202 or thesupplemental data analysis element 206. Further, the configurationelement 202 and the supplemental data analysis element 206 of thepresent embodiment suitably allow the operator to change theconfiguration data according to the operator's wishes or the test system100 requirements. When the configuration data have been selected, theconfiguration data may be associated with relevant criteria and storedfor future use as default configuration data. For example, if theoperator selects a certain section group for a particular kind ofcomponents 106, the computer 108 may automatically use the same sectiongroup for all such components 106 unless instructed otherwise by theoperator.

[0054] The supplemental data analysis element 206 also provides forconfiguration and storage of the tester data file and additional data.The supplemental data analysis element 206 suitably allocates memory(step 426), such as a portion of the memory 112, for the data to bestored. The allocation suitably provides memory for all of the data tobe stored by the supplemental data analysis element 206, includingoutput test data from the tester data file, statistical data generatedby the supplemental data analysis element 206, control parameters, andthe like. The amount of memory allocated may be calculated according to,for example, the number of tests performed on the components 106, thenumber of section group arrays, the control limits, statisticalcalculations to be performed by the supplementary data analysis element206, and the like.

[0055] When all of the configuration data for performing thesupplementary analysis are ready and upon receipt of the output testdata, the supplementary data analysis element 206 loads the relevanttest data into memory (step 428) and performs the supplementary analysison the output test data. The supplementary data analysis element 206 mayperform any number and types of data analyses according to thecomponents 106, configuration of the test system 100, desires of theoperator, or other relevant criteria. The supplemental data analysiselement 206 may be configured to analyze the sections for selectedcharacteristics identifying potentially defective components 106 andpatterns, trends, or other characteristics in the output test data thatmay indicate manufacturing concerns or flaws.

[0056] The present supplementary data analysis element 206, for example,smoothes the output test data, calculates and analyzes variousstatistics based on the output test data, and identifies data and/orcomponents 106 corresponding to various criteria. The presentsupplementary data analysis element 206 may also classify and correlatethe output test data to provide information to the operator and/or testengineer relating to the components 106 and the test system 100. Forexample, the present supplementary data analysis element 206 may performoutput data correlations, for example to identify potentially related orredundant tests, and outlier incidence analyses to identify tests havingfrequent outliers.

[0057] The supplementary data analysis element 206 may include asmoothing system to initially process the tester data to smooth the dataand assist in the identification of outliers (step 429). The smoothingsystem may also identify significant changes in the data, trends, andthe like, which may be provided to the operator by the output element208.

[0058] The smoothing system is suitably implemented, for example, as aprogram operating on the computer system 108. The smoothing systemsuitably comprises multiple phases for smoothing the data according tovarious criteria. The first phase may include a basic smoothing process.The supplemental phases conditionally provide for enhanced trackingand/or additional smoothing of the test data.

[0059] The smoothing system suitably operates by initially adjusting aninitial value of a selected tester datum according to a first smoothingtechnique, and supplementarily adjusting the value according to a secondsmoothing technique if at least one of the initial value and theinitially adjusted value meets a threshold. The first smoothingtechnique tends to smooth the data. The second smoothing technique alsotends to smooth the data and/or improve tracking of the data, but in adifferent manner from the first smoothing technique. Further, thethreshold may comprise any suitable criteria for determining whether toapply supplemental smoothing. The smoothing system suitably compares aplurality of preceding adjusted data to a plurality of preceding rawdata to generate a comparison result, and applies a second smoothingtechnique to the selected datum to adjust the value of the selecteddatum according to whether the comparison result meets a firstthreshold. Further, the smoothing system suitably calculates a predictedvalue of the selected datum, and may apply a third smoothing techniqueto the selected datum to adjust the value of the selected datumaccording to whether the predicted value meets a second threshold.

[0060] Referring to FIG. 8, a first smoothed test data point is suitablyset equal to a first raw test data point (step 802) and the smoothingsystem proceeds to the next raw test data point (step 804). Beforeperforming smoothing operations, the smoothing system initiallydetermines whether smoothing is appropriate for the data point and, ifso, performs a basic smoothing operation on the data. Any criteria maybe applied to determine whether smoothing is appropriate, such asaccording to the number of data points received, the deviation of thedata point values from a selected value, or comparison of each datapoint value to a threshold. In the present embodiment, the smoothingsystem performs a threshold comparison. The threshold comparisondetermines whether data smoothing is appropriate. If so, the initialsmoothing process is suitably configured to proceed to an initialsmoothing of the data.

[0061] More particularly, in the present embodiment, the process startswith an initial raw data point R₀, which is also designated as the firstsmoothed data point S₀. As additional data points are received andanalyzed, a difference between each raw data point (R_(n)) and apreceding smoothed data point (S_(n−1)) is calculated and compared to athreshold (T₁) (step 806). If the difference between the raw data pointR_(n) and the preceding smoothed data point S_(n−1) exceeds thethreshold T₁, it is assumed that the exceeded threshold corresponds to asignificant departure from the smoothed data and indicates a shift inthe data. Accordingly, the occurrence of the threshold crossing may benoted and the current smoothed data point S_(n) is set equal to the rawdata point R_(n) (step 808). No smoothing is performed, and the processproceeds to the next raw data point.

[0062] If the difference between the raw data point and the precedingsmoothed data point does not exceed the threshold T₁, the processcalculates a current smoothed data point S_(n) in conjunction with aninitial smoothing process (step 810). The initial smoothing processprovides a basic smoothing of the data. For example, in the presentembodiment, the basic smoothing process comprises a conventionalexponential smoothing process, such as according to the followingequation:

S _(n)=(R _(n) −S _(n−1))*M ₁ +S _(n−1)

[0063] where M₁ is a selected smoothing coefficient, such as 0.2 or 0.3.

[0064] The initial smoothing process suitably uses a relatively lowcoefficient M₁ to provide a significant amount of smoothing for thedata. The initial smoothing process and coefficients may be selectedaccording to any criteria and configured in any manner, however,according to the application of the smoothing system, the dataprocessed, requirements and capabilities of the smoothing system, and/orany other criteria. For example, the initial smoothing process mayemploy random, random walk, moving average, simple exponential, linearexponential, seasonal exponential, exponential weighted moving average,or any other appropriate type of smoothing to initially smooth the data.

[0065] The data may be further analyzed for and/or subjected tosmoothing. Supplementary smoothing may be performed on the data toenhance the smoothing of the data and/or improve the tracking of thesmoothed data to the raw data. Multiple phases of supplementarysmoothing may also be considered and, if appropriate, applied. Thevarious phases may be independent, interdependent, or complementary. Inaddition, the data may be analyzed to determine whether supplementarysmoothing is appropriate.

[0066] In the present embodiment, the data is analyzed to determinewhether to perform one or more additional phases of smoothing. The datais analyzed according to any appropriate criteria to determine whethersupplemental smoothing may be applied (step 812). For example, thesmoothing system identify trends in the data, such as by comparing aplurality of adjusted data points and raw data points for preceding dataand generating a comparison result according to whether substantiallyall of the preceding adjusted data share a common relationship (such asless than, greater than, or equal to) with substantially all of thecorresponding raw data.

[0067] The smoothing system of the present embodiment compares aselected number P₂ of raw data points to an equal number of smootheddata points. If the values of all of the P₂ raw data points exceed (orare equal to) the corresponding smoothed data points, or if all raw datapoints are less than (or equal to) the corresponding smoothed datapoints, then the smoothing system may determine that the data isexhibiting a trend and should be tracked more closely. Accordingly, theoccurrence may be noted and the smoothing applied to the data may bechanged by applying supplementary smoothing. If, on the other hand,neither of these criteria is satisfied, then the current smoothed datapoint remains as originally calculated and the relevant supplementarydata smoothing is not applied.

[0068] In the present embodiment, the criterion for comparing thesmoothed data to the raw data is selected to identify a trend in thedata behind which the smoothed data may be lagging. Accordingly, thenumber of points P₂ may be selected according to the desired sensitivityof the system to changing trends in the raw data.

[0069] The supplementary smoothing changes the effect of the overallsmoothing according to the data analysis. Any appropriate supplementarysmoothing may be applied to the data to more effectively smooth the dataor track a trend in the data. For example, in the present embodiment, ifthe data analysis indicates a trend in the data that should be trackedmore closely, then the supplementary smoothing may be applied to reducethe degree of smoothing initially applied so that the smoothed data moreclosely tracks the raw data (step 814).

[0070] In the present embodiment, the degree of smoothing is reduced byrecalculating the value for the current smoothed data point using areduced degree of smoothing. Any suitable smoothing system may be usedto more effectively track the data or otherwise respond to the resultsof the data analysis. In the present embodiment, another conventionalexponential smoothing process is applied to the data using a highercoefficient M₂:

S _(n)=(R _(n) −S _(n−1))*M ₂ +S _(n−1)

[0071] The coefficients M₁ and M₂ may be selected according to thedesired sensitivity of the system, both in the absence (M₁) and thepresence (M₂) of trends in the raw data. In various applications, forexample, the value of M₁ may be higher than the value of M₂.

[0072] The supplementary data smoothing may include additional phases aswell. The additional phases of data smoothing may similarly analyze thedata in some manner to determine whether additional data smoothingshould be applied. Any number of phases and types of data smoothing maybe applied or considered according to the data analysis.

[0073] For example, in the present embodiment, the data may be analyzedand potentially smoothed for noise control, such as using a predictiveprocess based on the slope of the smoothed data. The smoothing systemcomputes a slope (step 816) based on a selected number P₃ of smootheddata points preceding the current data point according to anyappropriate process, such as line regression, N-points centered, or thelike. In the present embodiment, the data smoothing system uses a “leastsquares fit through” process to establish a slope of the preceding P₃smoothed data points.

[0074] The smoothing system predicts a value of the current smootheddata point according to the calculated slope. The system then comparesthe difference between the previously calculated value for the currentsmoothed data point (S_(n)) to the predicted value for the currentsmoothed data point to a range number (R₃) (step 818). If the differenceis greater than the range R₃, then the occurrence may be noted and thecurrent smoothed data point is not adjusted. If the difference is withinthe range R₃, then the current smoothed data point is set equal to thedifference between the calculated current smoothed data point (S_(n))and the predicted value for the current smoothed data point (S_(n−pred))multiplied by a third multiplier M₃ and added to the original value ofthe current smoothed data point (step 820). The equation:

S _(n)=(S_(n−pred) −S _(n))*M ₃ +S _(n)

[0075] Thus, the current smoothed data point is set according to amodified difference between the original smoothed data point and thepredicted smoothed data point, but reduced by a certain amount (when M₃is less than 1). Applying the predictive smoothing tends to reducepoint-to-point noise sensitivity during relatively flat (or otherwisenontrending) portions of the signal. The limited application of thepredictive smoothing process to the smoothed data points ensures thatthe calculated average based on the slope does not affect the smootheddata when significant changes are occurring in the raw data, i.e., whenthe raw data signal is not relatively flat.

[0076] After smoothing the data, the supplementary data analysis element206 may proceed with further analysis of the tester data. For example,the supplementary data analysis element 206 may conduct statisticalprocess control (SPC) calculations and analyses on the output test data.More particularly, referring again to FIGS. 4A-C, the supplemental dataanalysis element 206 may calculate and store desired statistics for aparticular component, test, and/or section (step 430). The statisticsmay comprise any statistics useful to the operator or the test system100, such as SPC figures that may include averages, standard deviations,minima, maxima, sums, counts, Cp, Cpk, or any other appropriatestatistics.

[0077] The supplementary data analysis element 206 also suitablyperforms a signature analysis to dynamically and automatically identifytrends and anomalies in the data, for example according to section,based on a combination of test results for that section and/or otherdata, such as historical data (step 442). The signature analysisidentifies signatures and applies a weighting system, suitablyconfigured by the operator, based on any suitable data, such as the testdata or identification of defects. The signature analysis maycumulatively identify trends and anomalies that may correspond toproblem areas or other characteristics of the wafer or the fabricationprocess. Signature analysis may be conducted for any desired signatures,such as noise peaks, waveform variations, mode shifts, and noise. In thepresent embodiment, the computer 108 suitably performs the signatureanalysis on the output test data for each desired test in each desiredsection.

[0078] In the present embodiment, a signature analysis process may beperformed in conjunction with the smoothing process. As the smoothingprocess analyzes the tester data, results of the analysis indicating atrend or anomaly in the data are stored as being indicative of a changein the data or an outlier that may be of significance to the operatorand/or test engineer. For example, if a trend is indicated by acomparison of sets of data in the smoothing process, the occurrence ofthe trend may be noted and stored. Similarly, if a data point exceedsthe threshold T₁ in the data smoothing process, the occurrence may benoted and stored for later analysis and/or inclusion in the outputreport.

[0079] For example, referring to FIGS. 6A-B, a signature analysisprocess 600 may initially calculate a count (step 602) for a particularset of test data and control limits corresponding to a particularsection and test. The signature analysis process then applies anappropriate signature analysis algorithm to the data points (step 604).The signature analysis is performed for each desired signaturealgorithm, and then to each test and each section to be analyzed. Errorsidentified by the signature analysis, trend results, and signatureresults are also stored (step 606). The process is repeated for eachsignature algorithm (step 608), test (step 610), and section (step 612).Upon completion, the supplementary data analysis element 206 records theerrors (step 614), trend results (step 616), signature results (step618), and any other desired data in the storage system.

[0080] Upon identification of each relevant data point, such as outliersand other data of importance identified by the supplementary analysis,each relevant data point may be associated with a value identifying therelevant characteristics (step 444). For example, each relevantcomponents or data point may be associated with a series of values,suitably expressed as a hexadecimal figure, corresponding to the resultsof the supplementary analysis relating to the data point. Each value mayoperate as a flag or other designator of a particular characteristic.For example, if a particular data point has failed a particular testcompletely, a first flag in the corresponding hexadecimal value may beset. If a particular data point is the beginning of a trend in the data,another flag may be set. Another value in the hexadecimal figure mayinclude information relating to the trend, such as the duration of thetrend in the data.

[0081] The supplementary data analysis element 206 may also beconfigured to classify and correlate the data (step 446). For example,the supplementary data analysis element 206 may utilize the informationin the hexadecimal figures associated with the data points to identifythe failures, outliers, trends, and other features of the data. Thesupplementary data analysis element 206 also suitably appliesconventional correlation techniques to the data, for example to identifypotentially redundant or related tests.

[0082] The computer 108 may perform additional analysis functions uponthe generated statistics and the output test data, such as automaticallyidentifying and classifying outliers (step 432). Analyzing each relevantdatum according to the selected algorithm suitably identifies theoutliers. If a particular algorithm is inappropriate for a set of data,the supplementary data analysis element 206 may be configured toautomatically abort the analysis and select a different algorithm.

[0083] The supplementary data analysis element 206 may operate in anysuitable manner to designate outliers, such as by comparison to selectedvalues and/or according to treatment of the data in the data smoothingprocess. For example, an outlier identification element according tovarious aspects of the present invention initially automaticallycalibrates its sensitivity to outliers based on selected statisticalrelationships for each relevant datum (step 434). Some of thesestatistical relationships are then compared to a threshold or otherreference point, such as the data mode, mean, or median, or combinationsthereof, to define relative outlier threshold limits. In the presentembodiment, the statistical relationships are scaled, for example byone, two, three, and six standard deviations of the data, to define thedifferent outlier amplitudes (step 436). The output test data may thenbe compared to the outlier threshold limits to identify and classify theoutput test data as outliers (step 438).

[0084] The supplementary data analysis element 206 stores the resultingstatistics and outliers in memory and identifiers, such as the x-y wafermap coordinates, associated with any such statistics and outliers (step440). Selected statistics, outliers, and/or failures may also triggernotification events, such as sending an electronic message to anoperator, triggering a light tower, stopping the tester 102, ornotifying a server.

[0085] In the present embodiment, the supplementary data analysiselement 206 includes a scaling element 210 and an outlier classificationelement 212. The scaling element 210 is configured to dynamically scaleselected coefficients and other values according to the output testdata. The outlier classification element 212 is configured to identifyand/or classify the various outliers in the data according to selectedalgorithms.

[0086] More particularly, the scaling element of the present embodimentsuitably uses various statistical relationships for dynamically scalingoutlier sensitivity and smoothing coefficients for noise filteringsensitivity. The scaling coefficients are suitably calculated by thescaling element and used to modify selected outlier sensitivity valuesand smoothing coefficients. Any appropriate criteria, such as suitablestatistical relationships, may be used for scaling. For example, asample statistical relationship for outlier sensitivity scaling isdefined as:

({square root}{square root over (1+NaturalLog _(Cpk) ²)})

[0087] Another sample statistical relationship for outlier sensitivityand smoothing coefficient scaling is defined as:

({square root}{square root over (1+NaturalLog _(Cpk) ²)})*Cpm

[0088] Another sample statistical relationship for outlier sensitivityand smoothing coefficient scaling is defined as:$\frac{\left( {\sigma*{Cpk}} \right)}{\left( {{Max} - {Min}} \right)},{{{where}\quad \sigma} = {{datum}\quad {Standard}\quad {Deviation}}}$

[0089] A sample statistical relationship used in multiple algorithms forsmoothing coefficient scaling is:${\frac{\sigma}{\mu}*10},{{{where}\quad \sigma} = {{{datum}\quad {Standard}\quad {Deviation}\quad {and}\quad \mu} = {{datum}\quad {Mean}}}}$

[0090] Another sample statistical relationship used in multiplealgorithms for smoothing coefficient scaling is:${\frac{\sigma^{2}}{\mu^{2}}*10},{{{where}\quad \sigma} = {{{datum}\quad {Standard}\quad {Deviation}\quad {and}\quad \mu} = {{datum}\quad {Mean}}}}$

[0091] The outlier classification element is suitably configured toidentify and/or classify components 106, output test data, and analysisresults according to any suitable algorithm the outliers in the outputtest data. The outlier classification element may also identify andclassify selected outliers and components 106 according to the testoutput test results and the information generated by the supplementaryanalysis element 206. For example, the outlier classification element issuitably configured to classify the components 106 intocritical/marginal/good part categories, for example in conjunction withuser-defined criteria; user-defined good/bad spatial patternsrecognition; classification of pertinent data for tester datacompression; test setup in-situ sensitivity qualifications and analyses;tester yield leveling analyses; dynamic wafer map and/or test stripmapping for part dispositions and dynamic retest; or test programoptimization analyses. The outlier classification element may classifydata in accordance with conventional SPC control rules, such as WesternElectric rules or Nelson rules, to characterize the data.

[0092] The outlier classification element suitably classifies the datausing a selected set of classification limit calculation methods. Anyappropriate classification methods may be used to characterize the dataaccording to the needs of the operator. The present outlierclassification element, for example, classifies outliers by comparingthe output test data to selected thresholds, such as valuescorresponding to one, two, three, and six statistically scaled standarddeviations from a threshold, such as the data mean, mode, and/or median.The identification of outliers in this manner tends to normalize anyidentified outliers for any test regardless of datum amplitude andrelative noise.

[0093] The outlier classification element analyzes and correlates thenormalized outliers and/or the raw data points based on user-definedrules. Sample user-selectable methods for the purpose of part andpattern classification based on identified outliers are as follows:

[0094] Cumulative Amplitude, Cumulative Count Method:${Count}_{LIMIT} = {\mu_{OverallOutlierCount} + \left( \frac{3*\left( \sigma_{OverallOutlierCount}^{2} \right)}{\left( {{Max}_{OverallOutlierCount} - {Min}_{OverallOutlierCount}} \right)} \right)}$${NormalizedOutlierAmplitude}_{LIMIT} = {\mu_{OverallNormalizedOutlierAmplitude} + \left( \frac{3*\left( \sigma_{OverallNormalizedOutlierAmplitude}^{2} \right)}{\begin{matrix}\left( {{Max}_{OverallNormalizedOutlierAmplitude} -} \right. \\\left. {Min}_{OverallNormalizedOutlierAmplitude} \right)\end{matrix}} \right)}$

[0095] Classification Rules:

Part_(CRITICAL)=True,If└(Part_(Cumlative Outlier Count)>Count_(LIMIT))AND(Part_(Cumlative Normalized Outlier Amplitude)>NormalizedOutlierAmplitude_(LIMIT))┘

Part_(MARGINAL HighAmplitude)=True,If└(Part_(Cumlative Normalized Outlier Amplitude)>NormalizedOutlierAmplitude_(LIMIT))┘

Part_(MARGINAL HighCount)=True,If(Part_(CumlativeOutlierCount)>Count_(LIMIT))

[0096] Cumulative Amplitude Squared, Cumulative Count Squared Method:${Count}_{{LIMIT}^{2}} = {\mu_{{OverallOutlierCount}^{\quad 2}} + \left( \frac{3*\left( \sigma_{{OverallOutherCount}^{\quad 2}}^{2} \right)}{\left( {{Max}_{{OverallOutlierCount}^{\quad 2}} - {Min}_{{OverallOutlierCount}^{\quad 2}}} \right)} \right)}$${NormalizedOutlierAmplitude}_{{LIMIT}^{2}} = {\mu_{{OverallNormalizedOutlierAmplitude}^{\quad 2}} + \left( \frac{3*\left( \sigma_{{OverallNormalizedOutlierAmplitude}^{\quad 2}}^{2} \right)}{\begin{matrix}\left( {{Max}_{{OverallNormalizedOutlierAmplitude}^{\quad 2}} -} \right. \\\left. {Min}_{{OverallNormalizedOutlierAmplitude}^{\quad 2}} \right)\end{matrix}} \right)}$

[0097] Classification Rules:

Part_(CRITICAL)=True, If└(Part_(CumlativeOutlierCount) _(²)>Count_(LIMIT) _(²) )AND(Part_(CumlativeNormalizedOutlierAmplitude)_(²) >Normalized Outlier Amplitude_(LIMIT) _(²) )┘

Part_(MARGINAL HighAmplitude)=True,If└(Part_(CumlativeNormalizedOutlierAmplitude) _(²) >Normalized OutlierAmplitude_(LIMIT) _(²) )┘

Part_(MARGINAL HighCount)=True, If└(Part_(Cumlative Outlier Count) _(²)>Count_(LIMIT) _(²) )┘

[0098] N-Points Method:

[0099] The actual numbers and logic rules used in the following examplescan be customized by the end user per scenario (test program, test node,tester, prober, handler, test setup, etc.). σ in these examples=σrelative to datum mean, mode, and/or median based on datum standarddeviation scaled by key statistical relationships.

Part_(CRITICAL)=True,If[((Part_(COUNT6σ)+Part_(COUNT3σ))≧2)OR((Part_(COUNT2σ)+Part_(COUNT1σ))≧6)]

Part_(CRITICAL)=True,If[((Part_(COUNT6σ)+Part_(COUNT3σ))≧1)AND((Part_(COUNT2σ)+Part_(COUNT1σ))≧3)]

Part_(MARGINAL)=True,If[((Part_(COUNT6σ)+Part_(COUNT3σ)+Part_(COUNT2σ)+Part_(COUNT1σ))≧3)]

Part_(NOISY)=True,If[((Part_(COUNT6σ)+Part_(COUNT3σ)+Part_(COUNT2σ)+Part_(COUNT1σ))≧1)]

[0100] The supplementary data analysis element 206 may be configured toperform additional analysis of the output test data and the informationgenerated by the supplementary data analysis element 206. For example,the supplementary data analysis element 206 may identify tests havinghigh incidences of failure or outliers, such as by comparing the totalor average number of failures, outliers, or outliers in a particularclassification to one or more threshold values.

[0101] The supplementary data analysis element 206 may also beconfigured to correlate data from different tests to identify similar ordissimilar trends, for example by comparing cumulative counts, outliers,and/or correlating outliers between wafers or other data sets. Thesupplementary data analysis element 206 may also analyze and correlatedata from different tests to identify and classify potential criticaland/or marginal and/or good parts on the wafer. The supplementary dataanalysis element 206 may also analyze and correlate data from differenttests to identify user-defined good part patterns and/or bad partpatterns on a series of wafers for the purposes of dynamic test timereduction.

[0102] The supplementary data analysis element 206 is also suitablyconfigured to analyze and correlate data from different tests toidentify user-defined pertinent raw data for the purposes of dynamicallycompressing the test data into memory. The supplementary data analysiselement may also analyze and correlate statistical anomalies and testdata results for test node in-situ setup qualification and sensitivityanalysis. Further, the supplementary data analysis element maycontribute to test node yield leveling analysis, for example byidentifying whether a particular test node may be improperly calibratedor otherwise producing inappropriate results. The supplementary dataanalysis element may moreover analyze and correlate the data for thepurposes of test program optimization including, but not limited to,automatic identification of redundant tests using correlated results andoutlier analysis and providing additional data for use in analysis. Thesupplementary data analysis element is also suitably configured toidentify critical tests, for example by identifying regularly failed oralmost failed tests, tests that are almost never-fail, and/or testsexhibiting a very low Cpk.

[0103] The supplementary data analysis may also provide identificationof test sampling candidates, such as tests that are rarely or neverfailed or in which outliers are never detected. The supplementary dataanalysis element may also provide identification of the best order testsequence based on correlation techniques, such as conventionalcorrelation techniques, combined with analysis and correlation ofidentified outliers and/or other statistical anomalies, number offailures, critical tests, longest/shortest tests, or basic functionalityissues associated with failure of the test.

[0104] The supplementary data analysis may also provide identificationof critical, marginal, and good parts as defined by sensitivityparameters in a recipe configuration file. Part identification mayprovide disposition/classification before packaging and/or shipping thepart that may represent a reliability risk, and/or test time reductionthrough dynamic probe mapping of bad and good parts during wafer probe.Identification of these parts may be represented and output in anyappropriate manner, for example as good and bad parts on a dynamicallygenerated prober control map (for dynamic mapping), a wafer map used foroffline inking equipment, a test strip map for strip testing at finaltest, a results file, and/or a database results table.

[0105] Supplemental data analysis at the cell controller level tends toincrease quality control at the probe, and this final test yields. Inaddition, quality issues may be identified at product run time, notlater. Furthermore, the supplemental data analysis and signatureanalysis tends to improve the quality of data provided to the downstreamand offline analysis tools, as well as test engineers or otherpersonnel, by identifying outliers. For example, the computer 108 mayinclude information on the wafer map identifying a group of componentshaving signature analyses indicating a fault in the manufacturingprocess. Thus, the signature analysis system may identify potentiallydefective goods that went undetected using conventional test analysis.

EXAMPLE

[0106] Referring now to FIG. 10, an array of semiconductor devices arepositioned on a wafer. In this wafer, the general resistivity ofresistor components in the semiconductor devices varies across thewafer, for example due to uneven deposition of material or treatment ofthe wafer. The resistance of any particular component, however, may bewithin the control limits of the test. For example, the targetresistance of a particular resistor component may be 1000Ω +/−10%. Nearthe ends of the wafer, the resistances of most of the resistorsapproach, but do not exceed, the normal distribution range of 900Ω and1100Ω (FIG. 11).

[0107] Components on the wafer may include defects, for example due to acontaminant or imperfection in the fabrication process. The defect mayincrease the resistance of resistors located near the low-resistivityedge of the wafer to 1080Ω. The resistance is well over the 1000Ωexpected for a device near the middle of the wafer, but is still wellwithin the normal distribution range.

[0108] Referring to FIGS. 12A-B, the raw test data for each componentmay be plotted. The test data exhibits considerable variation, due inpart to the varying resistivity among components on the wafer as theprober indexes across rows or columns of devices. The devices affectedby the defect are not easily identifiable based on visual examination ofthe test data or comparison to the test limits.

[0109] When the test data is processed according to various aspects ofthe present invention, the devices affected by the defect may beassociated with outliers in the test data. The smoothed test data islargely confined to a certain range of values. The data associated withthe defects, however, is unlike the data for the surrounding components.Accordingly, the smoothed data illustrates the departure from the valuesassociated with the surrounding devices without the defect. The outlierclassification element may identify and classify the outliers accordingto the magnitude of the departure of the outlier data from thesurrounding data.

[0110] The output element 208 collects data from the test system 100,suitably at run time, and provides an output report to a printer,database, operator interface, or other desired destination. Any form,such as graphical, numerical, textual, printed, or electronic form, maybe used to present the output report for use or subsequent analysis. Theoutput element 208 may provide any selected content, including selectedoutput test data from the tester 102 and results of the supplementarydata analysis.

[0111] In the present embodiment, the output element 208 suitablyprovides a selection of data from the output test data specified by theoperator as well as supplemental data at product run time via thedynamic datalog. Referring to FIG. 7, the output element 208 initiallyreads a sampling range from the database 114 (step 702). The samplingrange identifies predetermined information to be included in the outputreport. In the present embodiment, the sampling range identifiescomponents 106 on the wafer selected by the operator for review. Thepredetermined components may be selected according to any criteria, suchas data for various circumferential zones, radial zones, randomcomponents, or individual stepper fields. The sampling range comprises aset of x-y coordinates corresponding to the positions of thepredetermined components on the wafer or an identified portion of theavailable components in a batch.

[0112] The output element 208 may also be configured to includeinformation relating to the outliers, or other information generated oridentified by the supplementary data analysis element, in the dynamicdatalog (step 704). If so configured, the identifiers, such as x-ycoordinates, for each of the outliers are assembled as well. Thecoordinates for the operator-selected components and the outliers aremerged into the dynamic datalog (step 706). The output element 208retrieves selected information, such as the raw test data and one ormore data from the supplementary data analysis element 206, for eachentry in the merged x-y coordinate array of the dynamic datalog (step708).

[0113] The retrieved information is then suitably stored in anappropriate output report (step 710). The report may be prepared in anyappropriate format or manner. In the present embodiment, the outputreport suitably includes the dynamic datalog having a wafer mapindicating the selected components on the wafer and theirclassification. Further, the output element 208 may superimpose wafermap data corresponding to outliers on the wafer map of the preselectedcomponents. Additionally, the output element may include only theoutliers from the wafer map or batch as the sampled output. The outputreport may also include a series of graphical representation of the datato highlight the occurrence of outliers and correlations in the data.The output report may further include recommendations and supportingdata for the recommendations. For example, if two tests appear togenerate identical sets of failures and/or outliers, the output reportmay include a suggestion that the tests are redundant and recommend thatone of the tests be omitted from the test program. The recommendationmay include a graphical representation of the data showing the identicalresults of the tests.

[0114] The output report may be provided in any suitable manner, forexample output to a local workstation, sent to a server, activation ofan alarm, or any other appropriate manner (step 712). In one embodiment,the output report may be provided off-line such that the output does notaffect the operation of the system or transfer to the main server. Inthis configuration, the computer 108 copies data files, performs theanalysis, and generates results, for example for demonstration orverification purposes.

[0115] The particular implementations shown and described herein areillustrative of the invention and its best mode and are not intended tootherwise limit the scope of the present invention in any way. Indeed,for the sake of brevity, conventional signal processing, datatransmission, and other functional aspects of the systems (andcomponents of the individual operating components of the systems) maynot be described in detail herein. Furthermore, the connecting linesshown in the various figures contained herein are intended to representexemplary functional relationships and/or physical couplings between thevarious elements. Many alternative or additional functionalrelationships or physical connections may be present in a practical testsystem. The present invention has been described above with reference toa preferred embodiment. However, changes and modifications may be madeto the preferred embodiment without departing from the scope of thepresent invention.

[0116] The present invention has been described with reference to apreferred embodiment. Changes and modifications may be made, however,without departing from the scope of the present invention. These andother changes or modifications are intended to be included within thescope of the present invention, as expressed in the following claims.

1. A test system, comprising: a tester configured to test a componentand generate test data; and an outlier identification element configuredto receive the test data and identify an outlier in the test data.
 2. Atest system according to claim 1, wherein the outlier identificationelement is configured to operate in conjunction with a set ofconfiguration data in a recipe file.
 3. A test system according to claim1, wherein the test data corresponds to a section group of components ona wafer.
 4. A test system according to claim 1, wherein the outlieridentification element is configured to automatically calibrate asensitivity of the outlier identification element to the test data.
 5. Atest system according to claim 1, further comprising a data correlationelement configured to correlate the test data.
 6. A test systemaccording to claim 1, wherein the outlier identification element isconfigured to identify the outlier at run time.
 7. A test systemaccording to claim 1, further comprising a data smoothing elementconfigured to receive the test data and smooth the test data, andwherein the outlier identification element is configured to receive thesmoothed test data and identify the outlier in the smoothed test data.8. A data analysis system for semiconductor test data, comprising: asupplementary data analysis element configured to identify outliers inthe test data; and an output element configured to generate an outputreport including the identified outliers.
 9. A data analysis systemaccording to claim 8, wherein the supplementary data analysis element isconfigured to operate in conjunction with a set of configuration data ina recipe file.
 10. A data analysis system according to claim 8, whereinthe test data corresponds to a section group of components on a wafer.11. A data analysis system according to claim 8, wherein thesupplementary data analysis element is configured to automaticallycalibrate a sensitivity of the outlier identification element to thetest data.
 12. A data analysis system according to claim 8, wherein thesupplementary data analysis element includes a data correlation elementconfigured to correlate the test data.
 13. A data analysis systemaccording to claim 8, wherein the supplementary data analysis element isconfigured to identify the outliers at run time.
 14. A data analysissystem according to claim 8, wherein the supplementary data analysiselement includes a data smoothing element configured to receive the testdata and smooth the test data, and wherein the supplementary dataanalysis element is configured to identify the outliers in the smoothedtest data.
 15. A method for testing semiconductors, comprising:generating test data for multiple components; and identifying an outlierin the test data at run time.
 16. A method according to claim 15,further comprising reading configuration data from a recipe file,wherein identifying the outlier includes identifying the outlieraccording to the configuration data in the recipe file.
 17. A methodaccording to claim 15, wherein the test data corresponds to a sectiongroup of components on a wafer.
 18. A method according to claim 15,further comprising calibrating a sensitivity for identifying the outlierin the test data.
 19. A method according to claim 15, further comprisingsmoothing the test data.
 20. A method according to claim 15, furthercomprising correlating the test data to identify similarities in thetest data.